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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Grouping by a Timestamp and another column

The .resample method is unable to group by anything other than periods of time. The .groupby method, however, has the ability to group by both periods of time and other columns.

In this recipe, we will show two very similar but different approaches to group by Timestamps and another column.

How to do it…

  1. Read in the employee dataset, and create a DatetimeIndex with the HIRE_DATE column:
    >>> employee = pd.read_csv('data/employee.csv',
    ...     parse_dates=['JOB_DATE', 'HIRE_DATE'],
    ...     index_col='HIRE_DATE')
    >>> employee
                UNIQUE_ID  ...   JOB_DATE
    HIRE_DATE              ...
    2006-06-12          0  ... 2012-10-13
    2000-07-19          1  ... 2010-09-18
    2015-02-03          2  ... 2015-02-03
    1982-02-08          3  ... 1991-05-25
    1989-06-19          4  ... 1994-10-22
    ...               ...  ...        ...
    2014-06-09       1995  ... 2015-06-09...
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